In this section, we detail each part of our proposed SAFF, which aims to develop a solution that can both improve the transferability of cross-domain features and exploit class-discriminative information to generate labeled cross-domain representations that implicitly preserve class-discriminative information, thereby further narrowing the domain gap in UDA.
Fig. REF  illustrates our proposed SAFF. \(x_{s}\) , \(x_{i}\)  and \(x_{t}\)  are samples from source domain \(S = \left\lbrace  {\left( {{x_{sm}},{y_{sm}}} \right)} \right\rbrace _{m = 1}^{{N_s}}\) , SSID \(I = \left\lbrace  {\left( {{x_{in}},{y_{in}}} \right)} \right\rbrace _{n = 1}^{{N_s}}\)  and target domain \(T = \left\lbrace  {\left( {{x_{tp}}} \right)} \right\rbrace _{p = 1}^{{N_t}}\)  respectively, which are fed into the feature extractor in parallel, and \(I\)  is initialized by \(S\) . Where \(y \in \left\lbrace  {1,2, \ldots ,K} \right\rbrace \)  is the label of \(x\) , \(N\)  and \(K\)  denote the number of sample and class respectively. The Vision Transformer (ViT) [1]}, [2]} is used as the feature extractor to extract more general representations as its outstanding performance in many computer vision tasks [3]}, [4]}. Our feature extractor consists of \(L\)  feature extractor blocks followed by SSID learning strategy, which we described in detail in Section III. A. Then there is the max-pooling layer, the bottleneck layer, and the classifier head layer for classification. The specified output of the feature extractor and bottleneck layer are stored and updated in the external memory bank for subsequent loss function computation. The memory bank and loss functions are introduced in Sections III. B and III. C, respectively.
